Using microseismic data to characterize hydraulic fractures

ABSTRACT

Methods and apparatus that use microseismic event data, stress data, seismic data, and rock properties to predict the hydrocarbon production success of a well location are disclosed. An example method generates a hydrocarbon production function based on information associated with at least a first well location, obtains information associated with a second well location, and calculates the hydrocarbon production function using the information associated with the second well location to predict the hydrocarbon production of the second well location.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional application and claims benefit under 35U.S.C. §121 of U.S. patent application Ser. No. 11/350,639, filed onFeb. 9, 2006, entitled “METHODS AND APPARATUS FOR PREDICTING THEHYDROCARBON PRODUCTION OF A WELL LOCATION.”

FIELD OF THE DISCLOSURE

The present disclosure relates generally to predicting the hydrocarbonproduction success of a well location and, more specifically, to methodsand apparatus that use microseismic event data, information on in-situstress, and rock properties to predict the hydrocarbon productionsuccess of a well location and stimulation (e.g., due to a hydraulicfracture).

BACKGROUND

The collection and analysis of microseismic events associated withhydrofracturing a well to improve production or due to production fromreservoirs are generally well known. Such microseismic events areessentially small earthquakes (e.g., having a Richter magnitude of lessthan three) that result from stress changes within the geologicalstructures associated with a well or reservoir. Typically, these stresschanges are induced during the extraction or injection of fluids intothe well or reservoir. More specifically, the anisotropic nature ofearth stresses within a reservoir results in the accumulation of shearstresses on geological structures such as faults, fractures, etc. Theseaccumulated shear stresses are often released during depletion (e.g.,extraction processes) and stimulation (e.g., during hydraulic fracturestimulation) operations. The release of these shear stresses results inthe emission of acoustic energy or sound that can be detected by devicessuch as, for example, geophones, accelerometers, etc., and analyzed todetermine certain physical characteristics of the well and/or reservoir.

Some past efforts have attempted to analyze microseismic data tooptimize well placement and to predict well performance. In particular,some of these efforts have focused on identifying the locations ofmicroseismic events to map fractures to enable the prediction of wellperformance and/or optimize well placement. For example, microseismicdata may be analyzed to determine fracture orientation, extent or size,and estimated growth, all of which are factors that affect optimal wellplacement and, ultimately, well production or performance. One sucheffort is described in Society of Petroleum Engineers (SPE) paper number88695, entitled “Contribution to the Valuation of MicroseismicMonitoring Data Recorded from Treatment Well—Results Based on 20Hydro-fracturing Jobs Recorded From Treatment Well,” by Kaiser et al.,the disclosure of which is incorporated by reference herein in itsentirety.

Other efforts have focused on using microseismic event data to improvehydraulic fracture stimulation of a reservoir to thereby increase theproductivity of the associated well(s). One such effort is described inSPE paper number 91435, entitled “Successful Application of HydrajetFracturing on Horizontal Wells Completed in a Thick Shale Reservoir,” byEast et al., the disclosure of which is incorporated by reference hereinin its entirety.

While the above-noted uses of microseismic data have focused ondetermining the spatial characteristics of reservoirs (e.g., fracturelocation, orientation, extent, etc.), still other efforts have attemptedto use microseismic event data to estimate reservoir properties such as,for example, porosity, permeability, fluid saturation, stress, seismicvelocity, and rock strength. In addition to spatial characteristics,these other reservoir properties may be useful to control fluidextraction from a reservoir and/or to plan production and/or developmentof fields. An example system that processes microseismic signals toestimate reservoir properties as noted above is described in U.S. Pat.No. 6,947,843, the entire disclosure of which is incorporated byreference herein in its entirety.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow diagram representing an example process to predict thehydrocarbon production of a well location.

FIG. 2 represents an example manner in which rock petrophysicalproperties may be determined in the example process of FIG. 1.

FIG. 3 represents an example manner in which rock mechanical and stressproperties may be determined in the example process of FIG. 1.

FIG. 4 represents an example curvature of a productive layer of areservoir.

FIG. 5 is a flow diagram representing an example process to estimate ahydraulic fracture volume that may be used to determine hydraulicfracture characteristics in the example process of FIG. 1.

FIG. 6A is example representation of a fracture having a relatively highhorizontal stress anisotropy.

FIG. 6B is an example representation of a fracture network having arelatively low horizontal stress anisotropy.

FIG. 7 is an example graphical depiction of a relationship between thehorizontal stress characteristics of a fracture and the aspect ratio ofthe fracture.

FIG. 8 is an example processor system that may be used to executemachine readable instructions to implement the example systems andmethods described herein.

SUMMARY

In accordance with one disclosed aspect, a system and method ofpredicting a hydrocarbon production of a well location generates ahydrocarbon production function based on information associated with atleast a first well location, obtains information associated with asecond well location, and calculates the hydrocarbon production functionusing the information associated with the second well location topredict the hydrocarbon production of the second well location.

In accordance with another disclosed aspect, a system and method ofestimating a fracture volume obtains a set of microseismic dataassociated with a fracture, generates a voxelized space based on the setof microseismic data, and selects pairs of points from the set ofmicroseismic data. Additionally, the system and method identifies voxelsfrom the voxelized space, wherein the identified voxels correspond tothe pairs of points and vectors connecting the pairs of points, andestimates the fracture volume based on the identified voxels.

In accordance with still another disclosed aspect, a system and methodof estimating an aspect ratio of a fracture associated with a geologicalarea computes a stress ratio associated with the fracture, and maps thestress ratio to an estimated aspect ratio via a predeterminedrelationship relating stress ratios to aspect ratios for the geologicalarea

DETAILED DESCRIPTION

In general, the example methods, apparatus, and articles of manufacturedescribed herein use rock properties, stress and microseismic event dataor information collected, for example, during a hydraulic fracturetreatment to predict or estimate the hydrocarbon production success of awell location (e.g., a location that may be drilled). More specifically,the methods, apparatus, and articles of manufacture described hereindetermine geomechanical, petrophysical, and/or other rock propertiesthat govern hydrocarbon production for a horizon, field, or geologicalarea (e.g., a basin), and then use the results to predict theproductivity of well locations for future wells.

In the examples described herein, a hydrocarbon production function ormodel is determined or generated by fitting data associated withgeomechanical, petrophysical, and/or other rock properties for one ormore operating or existing wells to the actual hydrocarbon production ofthose operating wells. The operating or existing well(s) used todetermine or generate the hydrocarbon production function may beassociated with a particular geological area (e.g., a basin). In thismanner, the hydrocarbon production of a location to be drilled in thegeological area to which the hydrocarbon production function applies canbe estimated by collecting geomechanical, petrophysical, and/or otherrock property information for the to be drilled location and using thiscollected data in conjunction with the hydrocarbon production functionto estimate or predict the hydrocarbon production of the to be drilledlocation. As described in greater detail below, some of the parametersused to determine and/or calculate the example hydrocarbon productionfunction described herein may be determined using microseismic data,seismic data, log data, etc.

Before discussing the example methods in detail, it should be recognizedthat the example methods or processes described herein may beimplemented as machine readable and executable instructions, code,software, etc. stored on a tangible medium such as, for example, amagnetic, solid state, and/or optical medium and executable by, forexample, a controller, microprocessor, etc., such as the exampleprocessor system 800 of FIG. 8 described in greater detail below.Further, some or all of the operations associated with the examplemethods described herein may be executed manually and/or the order ofthe operations may be varied or eliminated to achieve the same orsimilar results.

The example methods may be described in conjunction with flow diagrams,which may be representative of example machine readable and executableinstructions, software, or code. Such machine readable instructions,software, or code may comprise a program for execution by a processorsuch as the processor 812 shown in the example processor system 800 ofFIG. 8. The program may be embodied in software stored on a tangiblemedium such as a CD-ROM, a floppy disk, a hard drive, a digitalversatile disk (DVD), or a memory associated with the processor 812and/or embodied in firmware and/or dedicated hardware in a well-knownmanner. Additionally or alternatively, the example methods may beimplemented using any desired combination of hardware, firmware, and/orsoftware. For example, one or more integrated circuits, discretesemiconductor components, or passive electronic components may be usedto perform the operations represented in the flow diagrams.

Now turning to FIG. 1, a flow diagram representing an example process100 to predict the hydrocarbon production of a well location is shown.The example prediction process 100 begins by determining rockpetrophysical properties using log data, which may be calibrated withcore measurements (block 102). In general, the petrophysical propertiesdetermined at block 102 are related to the hydrocarbon productionpotential of the rock. As depicted in FIG. 2, the operations at block102 may be carried out using an elemental log analysis 200, which may bemore commonly referred to as ELAN™ (which is a mark of Schlumberger), todetermine what type of hydrocarbon is present in the pore space of therock, how much hydrocarbon is present in the pore space of the rock, andin what pore space the hydrocarbon is located. As is known, an elementallog analysis separates the minerals, porosity and saturation ofhydrocarbon for a volume of rock using log data 202 as inputs and coremeasurements 204 as calibration points. The elemental log analysis 200then outputs rock petrophysical properties 206 such as, for example,porosity, mineral volumes, hydrocarbon saturation, organic carboncontent, etc. As is known, the log data 202 may be collected using oneor more probes and/or other tools, sensors, etc. disposed within one ormore well borehole(s) and the core measurements 204 may be made underlaboratory conditions using core samples obtained during the drilling ofthe well(s). The core measurements 204 provide certain rock propertiesat known depths within the well and, thus, can be used in known mannersto better evaluate the rock properties associated with log datacollected at different depths (e.g., deeper) in the well.

Returning to FIG. 1, following the determination of the rockpetrophysical properties at block 102, the example process 100determines rock mechanical and stress properties (block 104). While anumber of techniques can be used to determine the rock mechanical andstress properties for a particular well, the operations associated withblock 104 may be advantageously carried out using a mechanical earthmodeling technique such as that shown in FIG. 3.

The earth modeling technique 300 depicted in FIG. 3 is a well-knowntechnique developed by Schlumberger Technology Corporation. Moredetailed information describing earth modeling techniques are disclosedin U.S. Pat. Nos. 6,549,854 and 6,766,354, both of which areincorporated herein by reference in their entireties. In general, theearth modeling technique 300 enables the generation of a one dimensionalmechanical earth model for the field associated with the well underanalysis. The one dimensional earth model may be used to evaluate rockmechanical and stress properties at the well borehole. In combinationwith seismic data, a three dimensional mechanical earth model coveringthe area of interest may also be generated and populated with well dataand seismic data using geostatistical techniques such as, for example,kriging. Such a three dimensional earth model can be particularly usefulto predict the expected production and performance of stimulationtreatments at locations for which there is no well information. Morespecifically, the model includes earth stresses or stress profiles suchas the pressure of fluids in rock pores or pore pressure (Pp) 302, theweight of the overburden or vertical stress (Sv) 304, the minimumeffective horizontal stress (Sh) 306, and the maximum effectivehorizontal stress (SH) 308. The mechanical earth model 300 also includesthe principal stress directions 310 such as, for example, the azimuthsof the stresses Sh and SH. In addition, the mechanical earth model 300includes rock mechanical properties such as rock compressional andtensile strength 312, Poisson's ratio, Young's modulus (i.e., the staticelastic properties of the rock), friction angle, etc.

Returning again to FIG. 1, following determination of the rockmechanical and stress properties at block 104, the example process 100determines the formation and horizon curvature from seismic or horizonproperties over productive layers (block 106). Curvature is the rate ofchange of angle along a surface (e.g., time or depth) with respect tonormal vectors along the surface. FIG. 4 depicts an example curvedsurface and the sign convention for curvature. In particular, FIG. 4illustrates regions of zero curvature, negative curvature, and positivecurvature. Curvature of a three dimensional surface (such as onebounding a hydrocarbon zone associated with a well) is related to stress(assuming buckling) as set forth in Equation 1 below, where the constantof proportionality can be determined using well, seismic, and stressinformation in the area.

$\begin{matrix}{{{Stress} \propto \frac{h \times K \times E}{2}}{where}{h = {{Layer}\mspace{14mu}{Thickness}}}{K = {{Layer}\mspace{14mu}{or}\mspace{14mu}{Horizon}\mspace{14mu}{Curvature}}}{E = {{{Young}'}s\mspace{14mu}{Modulus}}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

Referring again to FIG. 1, following the formation and horizon curvaturedetermination at block 106, the example prediction process 100determines the hydraulic fracture characteristics associated with theexisting well location under analysis (e.g., an operational well) (block108). More specifically, at block 108, microseismic event data, whichmay be collected during hydraulic fracture stimulation of the existingwell location, may be used to determine hydraulic fracture orientation,hydraulic fracture volume, hydraulic fracture aspect ratios, as well asany other desired hydraulic fracture characteristics.

To determine the hydraulic fracture volume at block 108, a discretepair-wise linear interpolation approach may be used. One particularlyuseful discrete pair-wise linear interpolation process is outlined belowin detail. However, before providing a more detailed description of themanner in which this linear interpolation process may be carried out, amore general discussion of the operation of the process is provided tofacilitate an understanding of the detailed example mathematicaloperations that may be used to implement the processes associated withblock 108.

Generally, the example process for estimating or determining hydraulicfracture volume at block 108 is based on the assumption thatmicroseismic events occurring near in time to the initiation of ahydraulic fracture stimulation are spatially closer to the source of thefracture than those microseismic events occurring relatively later intime from the initiation of the stimulation. In other words, for any setof microseismic data, the data is generally assumed to be temporally andspatially correlated such that data occurring later in time are morespatially distant from the source. Of course, in practice, some data maynot conform perfectly to the assumed spatial/temporal correlation.However, such non-conforming data would have or could be made to haveminimal, if any, effect on the resultant fracture volume estimate. Forexample, data deemed to be non-compliant or otherwise aberrant could beeliminated from consideration, processing, etc.

Given the assumed spatial/temporal correlation of the microseismic datato be processed, the data is initially received in a time ordered listsuch that data that is adjacent in the list is also temporally (andassumed to be spatially) adjacent. The list of data is then traversed todetermine the minimum and maximum x, y, and z axis coordinates, whichare in turn used to compute the maximum dimensions of a threedimensional space occupied by the microseismic data. The threedimensional space is then voxelized using a desired resolution (i.e.,voxel size) and may be represented using one or more data arrays and/orany other suitable data structure or construct. The use of such a datastructure (e.g., data arrays) enables the voxelized space to berepresented and stored in a computer memory and/or any other type ofcomputer readable medium.

After having established the voxelized space, the time ordered list ofmicroseismic data is processed to enable voxels within the voxelizedspace to be infilled, marked, tagged, or otherwise identified ascomposing the fracture space. In general, this identification processinvolves iteratively processing the time ordered list of microseismicdata to repeatedly select different pairs of data points that aresufficiently temporally and spatially correlated and infilling, tagging,etc. those voxels in the voxelized space corresponding to the datapoints themselves as well as the voxels lying along a vector joining thedata points. Thus, by repeatedly selecting different pairs of pointsfrom the time ordered list and infilling, tagging, etc. those voxelscorresponding to the original microseismic points themselves as well asthe voxels lying along the vectors connecting those points, thevoxelized space forms an infilled or tagged voxel volume, cloud, orspace within the overall or total available voxelized space. Thisinfilled or tagged voxel volume or space can then be associated with ormay correspond to the fracture volume.

Although it is possible to pair every original microseismic point withevery other such point during the above-described iterative process, theresulting volume of tagged voxels would substantially overestimate theactual volume of the associated fracture network. Thus, it isadvantageous to limit the extent to which points may be paired,corresponding to the assumed ranges of spatial/temporal correlation overwhich the point pairings are assumed to be valid or meaningful. Thus, inthe voxel infilling process described in greater detail below, pairs ofpoints that are temporally spaced beyond a predetermined threshold(e.g., a temporal spacing selected by a user) are not subjected to theinfilling or tagging process, and the points lying on the vectorconnecting these pairs of points are neither infilled nor tagged.Further, the example process below also recognizes that the degree ofcorrelation between pairs of points may decay or decrease withincreasing temporal separation. In particular, the example infillingprocess establishes a maximum radius or spatial correlation length maydecrease with increasing temporal lag. A pair of points that fallswithin the maximum temporal lag threshold but for which the distancebetween the points exceeds the maximum radius or correlation length isnot subjected to infilling or tagging.

A flow diagram generally representing an example of the above-describedprocess is provided in FIG. 5. The example process 500 for estimating ahydraulic fracture volume may be used to implement the example processof FIG. 1 and, in particular, the operations of block 108 shown therein.Turning in detail to FIG. 5, the example process obtains time orderedmicroseismic data (block 502). The time ordered microseismic data may bereceived in the form of a pre-processed list or one dimensional array oftime ordered data. The list of time ordered data is then processed orexamined to generate a voxelized space (block 504) to be used to holddata representing the fracture volume. In particular, as noted above,the voxelized space may be implemented as one or more three dimensionaldata arrays.

A pair of points is then selected from the time ordered microseismicdata (block 506) and the points are evaluated to determine if they fallwithin predetermined spatial and temporal thresholds (block 508). If thepoints do not fall within the thresholds at block 508, the pair ofpoints is not processed further and control returns to block 506 toselect a different pair of points. If the points do fall within thethresholds at block 508, the voxels associated with the points aretagged, infilled, or otherwise identified or classified as composing apart of the fracture volume (block 510). The process 500 then determinesif there are more point pairs to process (block 512). If there are morepoints to process at block 512, control is returned to block 506 toselect a different pair of points. If there are no further points toprocess at block 512, the example process 500 may then evaluate the setof tagged, infilled, etc. voxels to estimate the volume of the fracture(block 514).

The following discussion provides a more detailed example of theabove-described operations or processes for estimating hydraulicfracture volume. Initially, given N spatially and temporally correlatedpoints, P_(n)=[x_(n),y_(n),z_(n),t_(n)] in ascending time (t) order,where n=1 to N, with associated discretization intervals (Δx, Δy, Δz)>0,additional points are generated using discretized linear interpolationbetween pairs of points P_(n) and P_(n-l) for l=1 to L, where L<N and Lis subject to the constraints shown below.Δt<Δt_(max),r<R_(max)and:Δt≡t _(n) −t _(n-l)r≡[(x _(n) −x _(n-l))²+(y _(n) −y _(n-l))²+(z _(n) −z _(n-l))²]^(1/2)

The input points are then discretized by voxelizing them into athree-dimensional array. The entire list of points (i.e., the list of Npoints) is initially traversed to determine the numerical range of eachcoordinate (x_(min), x_(max), y_(min), y_(max), z_(min), z_(max)). Thedimensions of the three-dimensional array (n_(i),n_(j),n_(k)) are thendetermined as:n _(i)=(y _(max) −y _(min))/Δy+1.5n _(j)=(x _(max) −x _(min))/Δx+1.5n _(k)=(z _(max) −z _(min))/Δz+1.5

Two three-dimensional arrays are then allocated so that one of thearrays (T_(ijk)) is used to record the t coordinate values and the otherarray (M_(ijk)) is used to count the number of contributors to eachvoxel. After initializing both three-dimensional arrays to zero, eachinput point is voxelized by computing the indices i,j,k of thecorresponding cell in the three-dimensional array then recording t_(n)and the number of contributors. An example process by which the arrayscan be zeroed and input points can be voxelized is set forth below.

for n=1 to N:  T_(ijk) ← 0  M_(ijk) ← 0 end for for n=1 to N:  i ←(y_(n) − y_(min))/Δy + 0.5  j ← (x_(n) − x_(min))/Δx + 0.5  k ← (z_(n) −z_(min)) /Δz + 0.5  T_(ijk) ← T_(ijk) + t_(n)  M_(ijk) ← M_(ijk) + 1 endfor

If the voxel radius (Δr

Δx ² +Δy ² +Δz ²)^(1/2) exceeds the minimum distance between points,voxelization results in decimation and the total number of populatedvoxels, Np, is less than the total number of input points N. In thiscase, the array T_(ijk) is normalized by dividing by the array M_(ijk)and resetting M_(ijk) to unity before interpolation is performed.

Following voxelization, interpolation is performed to infill voxelsalong the vector joining each pair of points. An arbitrary maximum lag(L) is selected (e.g., by a user) based on an assumed temporalcorrelation length (Δt_(max)) and an average time interval betweenpoints (Δt). For example, L may be selected based on the equation L=NΔt_(max)/(t _(N) −t _(l)). A maximum radius (R_(max)), corresponding tolateral and vertical spatial correlation lengths is also assumed.Estimates of the temporal and spatial correlation lengths may beobtained by analyzing variograms generated using the microseismic dataassociated with the existing well location. Assuming that the degree ofcorrelation between pairs of points decays with increasing temporalseparation, the maximum radius may be modeled as a function of thelag-(l). For example,

${{r_{\max}(l)} = \frac{R}{l^{q}}},$where q>0, yields a maximum radius which decreases with increasing lag.A process by which the above-described interpolation may be performed isdescribed below.

for l = 1 to L:  r_(max) ← R_(max)/ l^(q)   for n= l+1 to N:    x ←x_(n) − x_(n−l)    y ← y_(n) − y_(n−l)    z ← z_(n) − z_(n−l)    r ←(x²+ y²+ z²)^(1/2)    if (r < r_(max) and t_(n) − t_(n−l) < Δt_(max))then     infill_voxels_between_pts (n, n−l)    end if   end for end for

Voxel infilling is then performed via linear interpolation byiteratively stripping segments of length Δr from the vector joiningpoints n and n−1 as shown in the example process below.

being infill_voxels_between_pts (n, n − l): x ← x_(n−l) y ← y_(n−l) z ←z_(n−l) {close oversize brace} set P = P_(n−l) t ← t_(n−l) r ← Δr/r }set length fraction (0 < r ≦ 1) while (r < 1): x ← x + r(x_(n) − x)interpolate a new point by y ← y + r(y_(n) − y) shifting P closer toP_(n) a z ← z + r(z_(n) − z) {close oversize brace} distance Δr t ← t +r (t_(n) − t) r ← Δr/[(x_(n)−x)²+(y_(n)−y)²+(z_(n)−z)²]^(1/2) } resetthe length fraction i ← (y − y_(min))/Δy + 0.5 j ← (x − x_(min))/Δx +0.5 {close oversize brace} voxelize P k ← (z − z_(min))/Δz + 0.5 T_(ijk) ← T _(ijk) + t {close oversize brace} record P in 3-D arrays M_(ijk) ← M _(ijk) + 1 end while end infill_voxels_between_pt

Following the determination of hydraulic fracture characteristics atblock 108, the example process 100 compares the hydraulic fracturecharacteristics (e.g., hydraulic fracture volume, orientation, and/oraspect ratios) to the stress and seismic anisotropy characteristics(block 110) of a fracture or fracture network. In particular, at block110, the example process 100 may compare the orientation and/or aspectratio information to stress characteristics such as, for example, stressanisotropy and/or seismic anisotropy characteristics.

The aspect ratio of a fracture or fracture network is generallypositively (and strongly) correlated to hydrocarbon production of thatfracture or fracture network. Thus, as described below, analyses ofmicroseismic information or data to determine the aspect ratio of anexisting well may be advantageous when determining or generating ahydrocarbon production function or model for use in predicting theproduction of new well locations. Before turning to a more detaileddiscussion concerning the manner in which fracture aspect ratios can bedetermined using microseismic data, a general discussion concerning thegeneral relationships between the anisotropy of in-situ stress fields,fracture aspect ratios, fracture growth, and fracture characteristics ortype is provided in connection with FIGS. 6A and 6B.

As can be seen from FIG. 6A, high stress (and seismic) anisotropy (e.g.,the ratio Sh/SH is closer to zero) results in the growth of asubstantially planar hydraulic fracture, which is commonly referred toas a classic hydraulic fracture. As depicted in FIG. 6A, in a classichydraulic fracture, the stress Sh (i.e., the minimum horizontal stress)is substantially smaller than the stress SH (i.e., the maximumhorizontal stress), which tends to result in fracture growth along themaximum stress direction in response to hydraulic fracturingstimulation. On the other hand, as shown in FIG. 6B, low stress (andseismic) anisotropy (e.g., the ratio of Sh/SH is closer to 1) typicallyresults in a wide fracture fairway composed of a more dispersed networkof intersecting fractures. Wide fracture fairways or fracture networksare generally advantageous (e.g., more productive) in low permeabilityreservoirs such as the well-known Barnett shale, for example, becausethere is more contact area between the multiple fractures and thehydrocarbon bearing rock than occurs for a substantially planarfracture. Thus, a well location having a relatively high Sh/SH ratiotypically has a relatively high aspect ratio (i.e., width/length) andcan be expected to provide a wide fracture network such as that shown inFIG. 6B and to yield a relatively high hydrocarbon production.

As a result of stress field anisotropy, hydraulic fractures do not growisotropically, but instead have a preferred orientation and width.Hydraulic fracture width corresponds generally to the area of contactbetween the fracture and the formation, while the fracture orientationis generally a function of the principal stress directions acting on thefracture. The orientation and width of a hydraulic fracture may becomputed using the radius of gyration matrix defined as shown below.

$R = \begin{pmatrix}R_{11} & R_{12} & R_{13} \\R_{21} & R_{22} & R_{23} \\R_{31} & R_{32} & R_{33}\end{pmatrix}$ where$R_{ij} = {\frac{1}{N}{\sum\limits_{k = 1}^{N}\;{\left( {r_{i}^{(k)} - {\overset{\_}{r}}_{l}^{(k)}} \right){\left( {r_{j}^{(k)} - {\overset{\_}{r}}_{j}^{(k)}} \right).}}}}$

In the above computation, N represents the number of microseismic eventsrecorded during the monitoring of the hydrofracture growth, r_(i) ^((k))is the ith component of the position vector of the kth microseismicevent, and r_(i) ^(−(k)) is the mean value of r_(i) ^((k)) averaged overall the microseismic events and is the ith component of the positionvector of the center of gravity of the microseismic cloud. The squareroots of the eigenvalues of R are the principal radii of gyration andmay be considered as the principal axes (i.e., the width, length, andheight) of an ellipsoid describing the shape of the microseismic cloud.The eigenvectors of R define the directions of principal axes of themicroseismic cloud and can be used to determine the direction of theprincipal stress directions and the principal axes of the seismicanisotropy. Typically, one of the principal axes is substantiallyvertical, and the eigenvalues will be denoted by λV, λH and λh, where λVis the vertical eigenvalue, λH is the largest horizontal eigenvalue, andλh is the smallest horizontal eigenvalue. The aspect ratio (α) of themicroseismic cloud is then defined as square root of the smallesthorizontal eigenvalue divided by the largest horizontal eigenvalue, andis equal to α=(λ_(h)/λ_(H))^(1/2) and defines the width of the fracturefairway in terms of its length and the magnitude of the seismicanisotropy, which decreases with increasing aspect ratio. A secondaspect ratio β=(λ_(v)/λ_(H))^(1/2) may be computed and is related to thevertical extent of the hydrofracture and may be used to determine if thehydrofracture has stayed in zone.

This relationship can be calibrated (i.e. the single parameter p can bedetermined using, for example, the procedure or technique described indetail below in connection with FIG. 7) by calculating the ratio of thewidth to the length of the microseismic cloud at any location whereSh/SH is known (or estimated using, for example, three-dimensionalseismic data for azimuthal anisotropy) and the rock is approximatelyazimuthally isotropic such as, for example, in a region with smallfracture density or low curvature. The aspect ratio of any microseismiccloud at any location where a well has not yet been drilled, but atwhich Sh/SH can be estimated, can then be predicted so that thisestimate can be used to estimate the volume of the microseismic cloudbefore the well is drilled. It should be noted that this method can alsobe used to estimate the maximum horizontal stress at the location of ahydraulic fracture by combining the aspect ratio of the microseismiccloud with the minimum horizontal stress at that location according tothe above equation.

The operations associated with blocks 102-110 may be carried out for oneor more wells for which actual hydrocarbon production is known. The oneor more wells may be associated with a particular geological area (e.g.,a basin) for which the hydrocarbon production of a new (i.e., to bedrilled) well location is to be estimated or predicted. In this manner,as described in greater detail below, an equation or model relating(e.g., fitting) the data or information determined at blocks 102-110 canbe based on a more statistically significant data set and, thus, mayenable more accurate predictions of the hydrocarbon production of a newwell location within the same geological area or a geologically similararea.

In particular, the data or information determined at blocks 102-110 maybe related to the actual hydrocarbon production (block 112) for each ofthe existing well locations analyzed at blocks 102-110. As representedbelow, using rock properties, petrophysical properties, reservoircurvature, along with the microseismic orientation, volume, and aspectratios computed using the microseismic events, a correlation can bedetermined relating the properties to the hydrocarbon production suchthat:Hydrocarbon production=f(HIP,Sh,SH,Curvature,MSV,aspect ratio)

where

HIP=hydrocarbon in place

Sh=minimum horizontal stress

SH=maximum horizontal stress

Curvature=productive formation surface curvature

MSV=microseismic fracture volume

aspect ratio=aspect ratio of microseismic cloud

The correlation or relation of the above-noted parameters will varydepending on the particular characteristics of a geological area (e.g.,a basin) being analyzed. Each of the parameters or properties above may,for example, be determined for one or more of wells in a particulargeological area for which hydrocarbon production is known. Using theparameter values for each of the wells together with the knownhydrocarbon production of these wells, a best correlation of theparameters of interest (e.g., those noted above) can be determined atblock 112 using one of several data fitting methods or techniques. Forexample, a least squares, weighted average, linear regression, or anyother suitable data fitting technique may be used to find an optimal fitof the data to a function. However, it should be noted that thehydrocarbon function or model described above is one example function ormodel and that fewer parameters and/or additional parameters may be usedto generate the function or model.

There also exists a relationship (material balance) between themechanical/stress properties and microseismic fracture volume such thatMSV=f (Sh, SH, Curvature, volume of fracture fluid). Thus, developmentof this relationship using the techniques described herein providesanother manner in which the MSV parameter may be calculated for a newwell location (e.g. a location to be drilled). The MSV can also beestimated at any location where Sh/SH is known or can be estimated(e.g., via analysis of three-dimensional seismic data for azimuthalanisotropy) using, for example, the technique described below inconnection with FIG. 7.

After determining or generating a hydrocarbon production function ormodel associated with a particular geological area (e.g., a basin) atblock 112, the example process 100 uses the hydrocarbon productionfunction or model developed at block 112 to predict the hydrocarbonproduction of a new well location (e.g., a location that may be drilled)(block 114). More specifically, values for each of the parameterscomposing the function or model are determined for the new well locationand a predicted hydrocarbon production is computed. For the examplefunction or model provided above, values for HIP, Sh, SH, curvature,MSV, and aspect ratio (α) may be determined for the new well locationand used with the previously generated hydrocarbon production functionor model (i.e., the function or model generated at block 112) to computethe predicted hydrocarbon production (block 114).

As noted above, the aspect ratio of a new well location (e.g., a welllocation to be drilled) can be determined using more easily obtainablestress data as opposed to microseismic information. Specifically, theratio of minimum and maximum horizontal stress (i.e., Sh/SH) may berelated to the aspect ratio α. In particular, this relationship can beexpressed generally as α=(S_(h)/S_(H))^(P), where p is characteristic ofa particular geological area.

FIG. 7 is an example graph including a family curves illustrating therelationship between the stress ratio and aspect ratio for different pvalues. To predict, estimate, or determine the aspect ratio for a newwell location (e.g., a location to be drilled), the actual stress dataand aspect ratio information associated with existing well locations(e.g., information collected at blocks 108 and 110 of the process 100)is used to determine which of the family of curves best represents thegeological area (e.g., a basin or field). After the curve representativeof the geological area is selected from the family of curves shown inFIG. 7, stress data (i.e., Sh and SH) for the new well location (e.g.,the location to be drilled) are estimated or measured. The ratio Sh/SHis then calculated and mapped to the selected curve to determine anestimated aspect ratio. For example, if the ratio Sh/SH for a new welllocation is determined to be 0.8 and the p value associated with thatlocation is determined to be 0.5, then using the example graph of FIG.7, the estimated or predicted aspect ratio for the new location is about0.9. The estimated aspect ratio can then be used (along with values forthe other parameters) when computing the predicted production for thenew well location using the production equation or model developed atblock 112 of FIG. 1.

FIG. 8 is a block diagram of an example processor system that may beused to implement the systems and methods described herein. As shown inFIG. 8, the processor system 800 includes a processor 812 that iscoupled to an interconnection bus 814. The processor 812 includes aregister set or register space 816, which is depicted in FIG. 8 as beingentirely on-chip, but which could alternatively be located entirely orpartially off-chip and directly coupled to the processor 812 viadedicated electrical connections and/or via the interconnection bus 814.The processor 812 may be any suitable processor, processing unit ormicroprocessor. Although not shown in FIG. 8, the system 800 may be amulti-processor system and, thus, may include one or more additionalprocessors that are identical or similar to the processor 812 and thatare communicatively coupled to the interconnection bus 814.

The processor 812 of FIG. 8 is coupled to a chipset 818, which includesa memory controller 820 and an input/output (I/O) controller 822. As iswell known, a chipset typically provides I/O and memory managementfunctions as well as a plurality of general purpose and/or specialpurpose registers, timers, etc. that are accessible to and/or used byone or more processors coupled to the chipset 818. The memory controller820 performs functions that enable the processor 812 (or processors ifthere are multiple processors) to access a system memory 824 and a massstorage memory 825.

The system memory 824 may include any desired type of volatile and/ornon-volatile memory such as, for example, static random access memory(SRAM), dynamic random access memory (DRAM), flash memory, read-onlymemory (ROM), etc. The mass storage memory 825 may include any desiredtype of mass storage device including hard disk drives, optical drives,tape storage devices, etc.

The I/O controller 822 performs functions that enable the processor 812to communicate with peripheral input/output (I/O) devices 826 and 828and a network interface 830 via an I/O bus 832. The I/O devices 826 and828 may be any desired type of I/O device such as, for example, akeyboard, a video display or monitor, a mouse, etc. The networkinterface 830 may be, for example, an Ethernet device, an asynchronoustransfer mode (ATM) device, an 802.11 device, a DSL modem, a cablemodem, a cellular modem, etc. that enables the processor system 800 tocommunicate with another processor system.

While the memory controller 820 and the I/O controller 822 are depictedin FIG. 8 as separate functional blocks within the chipset 818, thefunctions performed by these blocks may be integrated within a singlesemiconductor circuit or may be implemented using two or more separateintegrated circuits.

1. A method of estimating a fracture volume, comprising: obtaining a setof microseismic data associated with a fracture; generating a voxelizedspace based on the set of microseismic data; selecting pairs of pointsfrom the set of microseismic data that fall within at least one of aspatial or a temporal threshold; identifying voxels from the voxelizedspace, wherein the identified voxels correspond to the pairs of pointsand points linearly dependent on vectors connecting the pairs of points,by: infilling the identified voxels in the voxelized space by performinga linear interpolation using segments from the vectors connecting thepair of points; and estimating the fracture volume based on theidentified voxels.
 2. A method as defined in claim 1, wherein obtainingthe microseismic data associated with the fracture comprises obtaining atime ordered set of microseismic data.
 3. A method as defined in claim1, wherein obtaining the microseismic data associated with the fracturecomprises obtaining microseismic data associated with a hydraulicfracturing operation.
 4. A method as defined in claim 1, whereingenerating the voxelized space based on the set of microseismic datacomprises establishing at least one three dimensional array to representthe voxelized space.
 5. A system for estimating a fracture volume,comprising: a processor and a memory coupled to the processor, whereinthe processor is programmed to: obtain a set of microseismic dataassociated with a fracture; generate a voxelized space based on the setof microseismic data; select pairs of points from the set ofmicroseismic data that fall within at least one of a spatial or atemporal threshold; identify voxels from the voxelized space, whereinthe identified voxels correspond to the pairs of points and pointslinearly dependent on vectors connecting the pairs of points, by:infilling the identified voxels in the voxelized space by performing alinear interpolation using segments from the vectors connecting the pairof points; and estimate the fracture volume based on the identifiedvoxels.
 6. A system as defined in claim 5, wherein the processor isprogrammed to obtain the microseismic data associated with the fractureby obtaining a time ordered set of microseismic data.
 7. A system asdefined in claim 5, wherein the processor is programmed to obtain themicroseismic data associated with the fracture by obtaining microseismicdata associated with a hydraulic fracturing operation.
 8. A system asdefined in claim 5, wherein the processor is programmed to generate thevoxelized space based on the set of microseismic data by establishing atleast one three dimensional array to represent the voxelized space.
 9. Amachine readable medium having instructions stored thereon that, whenexecuted, cause a machine to: obtain a set of microseismic dataassociated with a fracture; generate a voxelized space based on the setof microseismic data; select pairs of points from the set ofmicroseismic data that fall within at least one of a spatial or atemporal threshold; identify voxels from the voxelized space, whereinthe identified voxels correspond to the pairs of points and pointslinearly dependent on vectors connecting the pairs of points, by:infilling the identified voxels in the voxelized space by performing alinear interpolation using segments from the vectors connecting the pairof points; and estimate the fracture volume based on the identifiedvoxels.
 10. A machine readable medium as defined in claim 9, wherein theinstructions, when executed, cause the machine to obtain themicroseismic data associated with the fracture by obtaining a timeordered set of microseismic data.
 11. A machine readable medium asdefined in claim 9, wherein the instructions, when executed, cause themachine to obtain the microseismic data associated with the fracture byobtaining microseismic data associated with a hydraulic fracturingoperation.
 12. A machine readable medium as defined in claim 9, whereinthe instructions, when executed, cause the machine to generate thevoxelized space based on the set of microseismic data by establishing atleast one three dimensional array to represent the voxelized space.